Automated object recognition kiosk for retail checkouts

ABSTRACT

A system, method, and apparatus for automated object recognition at checkout is provided. One checkout system includes a base, a head portion, a support for the head portion above the base, an illumination device in the head portion, at least one imaging device in the head portion, a processor coupled to the at least one imaging device, and a display. An examination space is defined between the base and the head portion for accommodating food items. The illumination device illuminates the food items, and the at least one imaging device captures color images of the food items. The processor applies a machine-learning model for performing image recognition of the food items in the color images to identify each food item. The image recognition is based on features of the food items that include shape, size, and color. An identification of each of the food items is presented on the display.

CLAIM OF PRIORITY

This application is a Continuation Application under 35 USC § 120 ofU.S. patent application Ser. No. 14/517,634, entitled “Automated ObjectRecognition Kiosk For Retail Checkouts,” filed on Oct. 17, 2014, whichclaims the priority benefit under 35 U.S.C. § 119(e) to U.S. ProvisionalApplication No. 61/891,902, filed on Oct. 17, 2013, all of which areincorporated herein by reference in their entirety.

TECHNICAL FIELD

The presently disclosed embodiments relate to retail stores, and moreparticularly to an object recognition kiosk for retail checkouts.

BACKGROUND

Retail outlets (e.g., supermarkets, cafeterias, etc.) offer sale ofvarious products and services. The outlets are typically equipped withself-checkout kiosks that allow a shopper to scan the products selectedfor purchase on their own to receive indicia of their prices. Theshopper may then use the indicia to make a payment for completing theproduct purchase.

The products are available either as packaged items or fresh items forpurchase. The packaged items typically carry identification markers suchas bar codes and radio frequency identification (RFID) tags, which arescanned by relevant scanners equipped with the self-checkout kiosks.However, the fresh items (e.g., freshly cooked meals such as differenttypes of curries, pastas, and breads; various salads; fresh fruits andvegetables; etc.) are often untagged and/or unpacked, and require astore attendant to intervene for enabling their purchase. The storeattendant traditionally uses his personal assessment of the type andnumber of ingredients for each fresh item and manually inputs theassessed information to a checkout kiosk for expected payment tocomplete the purchase.

Since the collection of fresh items at the retail outlets may changebased on customer demand or product offerings, the assessed informationmay vary based on related inventory knowledge and skill of the storeattendant. As a result, the assessed information may become susceptibleto error and hence business loss. The probability of erroneousassessment increases when various fresh items are mixed together basedon customer request or as a new product offering. Such assistedcheckouts for fresh items may also become labor intensive and timeconsuming based on the quantity of fresh items being checked out.Further, customer queues may become bottlenecks during peak periods ofcustomer demand, possibly to provoke the customers to leave the retailoutlet to shop elsewhere. Other sales may be lost from customers who maysimply avoid a retail location at known busy times and shop elsewhere,because of past inconvenience from delays.

Therefore, there exists a need for an automated object recognition kioskfor retail checkout of fresh foods and provide a seamless retailcheckout experience for better customer service.

SUMMARY

In view of the deficiencies in the conventional methodologies for retailcheckout at a kiosk, the disclosed subject matter provides a system,method, and apparatus for automated object recognition and checkout at aretail kiosk.

According to one aspect of the disclosed subject matter, a system forautomated retail checkout is provided. In an aspect, a controller of thesystem can be configured with a processor and a memory to controloperations of the automated retail checkout system. In other aspects, animaging device can be in communication with the controller andconfigured to create one or more electronic images of an object, such asa product for purchase. In further aspects, an object recognition devicecan be in communication with the controller and the imaging device. Theobject recognition device can be configured with a processor executingsoftware to receive electronic images from the imaging device, extractat least one feature from the one or more of the images, and recognizethe object based on a predetermined model of objects from an objectdatabase being applied to the feature from the one or more images. Inanother aspect, a display device can be configured with the system todisplay an indication from the object recognition device of therecognized object.

According to one embodiment of the disclosed subject matter, the atleast one feature extracted from the one or more images by the softwarecan be used by the processor to train the object recognition deviceusing a predetermined machine learning method that formulates the modelbased on recognizing the at least one feature from the object. Inanother embodiment, an illumination device can be configured by thecontroller to generate light having a predetermined level of brightnessand to illuminate the object using the generated light. In yet anotherembodiment, the object recognition device measures a change in lightingfrom a calibration pattern as perceived by the imaging device after theelectronic image of the object is created by the imaging device.According to another embodiment, the imaging device comprises a group ofmaneuverable cameras, and the controller can automatically calibratepositions of the group of cameras relative to the object based on thecalibration pattern. According to still another embodiment, the objectrecognition device analyzes the one or more electronic images from theimaging device and tracks a movement of a support structure incommunication with the object. In further embodiments, the controllercan adaptively tune the illumination device to generate light toilluminate the object based on the calibrated positions of the camerasand the position of the object. According to another embodiment, aweight sensor can be in communication with the controller and configuredto measure weight of the object.

According to another aspect of the disclosed subject matter, acomputer-implemented methodology for purchasing a product with a retailcheckout apparatus is provided. In an aspect, a methodology forcontrolling operations of the retail checkout apparatus with a computerincludes providing a processor for executing software instructions forilluminating, with an illumination device having a predetermined levelof brightness controlled by the computer, a predetermined region of theretail checkout apparatus. The methodology further includes capturing,with an imaging device controlled by the computer, one or more images ofa product located within the predetermined region; and recognizing, bythe computer, an identity of the product based on a predetermined modelbeing applied to the captured one or more images. In another aspect, themethodology includes providing, by the computer, an indication of therecognized product based on one or more predefined attributes of thedetermined product. In one aspect, the methodology includes displaying,by the computer on a display interface, at least a portion of theprovided indication for completing a purchase of the product.

According to another aspect of the disclosed subject matter, anapparatus for retail checkouts is provided. In an aspect, a head portionof the apparatus includes an illumination device and an imaging device.In another aspect, a base portion can be oriented a predetermineddistance below the head portion to create an object examination spacebetween the head portion and base portion. In other aspects, theillumination device can be configured to generate light within theobject examination space having a predetermined level of brightness andilluminate the object using the generated light, and the imaging devicecan be configured to create one or more electronic images of theilluminated object within the object examination space. In anotheraspect of the disclosure, an electronics portion, operationallyconnected to the imaging device and the illumination device, can includea processor programmed with software to execute instructions to receivethe one or more electronic images from the imaging device. The processoris further programmed with the software to extract at least one featurefrom the one or more images of the object, and recognize the objectbased on a predetermined model of objects from an object database beingapplied to the feature from the one or more electronic images. Inanother aspect of the apparatus, a display device can be operationallyconnected to the head portion and configured to display an indication ofthe object recognition from the software.

Other and further aspects and features of the disclosure will be evidentfrom reading the following detailed description of the embodiments,which are intended to illustrate, not limit, the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic that illustrates an exemplary object recognitionsystem, according to an embodiment of the present disclosure;

FIG. 2 is a perspective view of an exemplary automated objectrecognition kiosk, according to an embodiment of the present disclosure;

FIG. 3 is a front view of the exemplary automated object recognitionkiosk of FIG. 2, according to an embodiment of the present disclosure;and

FIG. 4 is a portion of the exemplary automated object recognition kioskof FIG. 2, according to an embodiment of the present disclosure.

DETAILED DESCRIPTION

The following detailed description is made with reference to thefigures. Preferred embodiments are described to illustrate thedisclosure, not to limit its scope, which is defined by the claims.Those of ordinary skill in the art will recognize a number of equivalentvariations in the description that follows.

Definitions

A “feature” is used in the present disclosure in the context of itsbroadest definition. The feature may refer to a property of an entitysuch as an image or an object. Examples of the property may include, butnot limited to, size, shape, brightness, color, and texture.

A “model” or “equation” is used in the present disclosure in the contextof its broadest definition. The model may refer to a mathematicalrepresentation involving one or more parameters, each of which maycorrespond to the feature.

Exemplary Embodiments

FIG. 1 is a schematic that illustrates an exemplary object recognitionsystem 100, according to an embodiment of the present disclosure. Someembodiments are disclosed in the context of an automated objectrecognition kiosk for retail checkout, e.g., in a cafeteria involvingrecognition of fresh foods including, but not limited to, fresh fruitsand vegetables, dairy products, freshly prepared eatables such ascurries, breads, pastas, salads, and burgers; or any combinationthereof. However, other embodiments may be applied in the context ofvarious business scenarios involving object recognition. Examples ofsuch scenarios may include, but not limited to, self-checkout ofproducts by customers in a supermarket, fast food restaurants, or coffeeshops; multi-product packaging of diversified products in a packagingplant; product quality control in a manufacturing plant; advanced driverassistance systems such as automatic parking systems; publicsurveillance systems; and automatic teller machines (ATMs).

The object recognition system 100 may represent any of a wide variety ofdevices capable of providing automated object recognition services tovarious devices. The object recognition system 100 may be implemented asa standalone and dedicated “black box” including hardware and installedsoftware, where the hardware is closely matched to the requirementsand/or functionality of the software. In some embodiments, the objectrecognition system 100 may enhance or increase the functionality and/orcapacity of a network to which it may be connected. The objectrecognition system 100 of some embodiments may include software,firmware, or other resources that support remote administration,operation, and/or maintenance of the object recognition system 100.

In one embodiment, the object recognition system 100 may be implementedas or in communication with any of a variety of computing devices (e.g.,a desktop PC, a personal digital assistant (PDA), a server, a mainframecomputer, a mobile computing device (e.g., mobile phones, laptops,etc.), an internet appliance, etc.). In some embodiments, the objectrecognition system 100 may be integrated with or implemented as awearable device including, but not limited to, a fashion accessory(e.g., a wrist band, a ring, etc.), a utility device (a hand-held baton,a pen, an umbrella, a watch, etc.), a body clothing, or any combinationthereof.

Other embodiments may include the object recognition system 100 beingimplemented by way of a single device (e.g., a computing device,processor or an electronic storage device 106) or a combination ofmultiple devices. The object recognition system 100 may be implementedin hardware or a suitable combination of hardware and software. The“hardware” may comprise a combination of discrete components, anintegrated circuit, an application-specific integrated circuit, a fieldprogrammable gate array, a digital signal processor, or other suitablehardware. The “software” may comprise one or more objects, agents,threads, lines of code, subroutines, separate software applications, twoor more lines of code or other suitable software structures operating inone or more software applications.

As illustrated, the object recognition system 100 may include acontroller 102 in communication with, or integrated with, interface(s)104, a storage device 106, and an object recognition device 108. Thecontroller 102 may execute machine readable program instructions forprocessing data (e.g., video data, audio data, textual data, etc.) andinstructions received from one or more devices such as the objectrecognition device 108, and so on. The controller 102 may include, forexample, microprocessors, microcomputers, microcontrollers, digitalsignal processors, central processing units, state machines, logiccircuits, and/or any devices that may manipulate signals based onoperational instructions. Among other capabilities, the controller 102may be configured to fetch and execute computer readable instructions inthe storage device 106 associated with the object recognition system100. In some embodiments, the controller 102 may be configured toconvert communications, which may include instructions, queries, data,etc., from one or more devices such as the object recognition device 108into appropriate formats to make these communications compatible with athird-party data application, network devices, or interfaces such asoutput devices, and vice versa. Consequently, the controller 102 mayallow implementation of the storage device 106 using differenttechnologies or by different organizations, e.g., a third-party vendor,managing the storage device 106 using a proprietary technology. In someother embodiments, the controller 102 may comprise or implement one ormore real time protocols (e.g., session initiation protocol (SIP),H.261, H.263, H.264, H.323, etc.) and non-real time protocols known inthe art, related art, or developed later to facilitate communicationwith one or more devices.

The object recognition system 100 may include a variety of known,related art, or later developed interface(s) 104, including softwareinterfaces (e.g., an application programming interface, a graphical userinterface, etc.); hardware interfaces (e.g., cable connectors, akeyboard, a card reader, a barcode reader, a biometric scanner, aninteractive display screen, a printer, etc.); or both. The interface(s)104 may facilitate communication between various devices such as thecontroller 102, the storage device 106, and the object recognitiondevice 108 within the object recognition system 100.

In some embodiments, the interface(s) 104 may facilitate communicationwith other devices capable of interacting with the object recognitionsystem 100 over a network (not shown). The network may include, forexample, one or more of the Internet, Wide Area Networks (WANs), LocalArea Networks (LANs), analog or digital wired and wireless telephonenetworks (e.g., a PSTN, Integrated Services Digital Network (ISDN), acellular network, and Digital Subscriber Line (xDSL)), radio,television, cable, satellite, and/or any other delivery or tunnelingmechanism for carrying data. Network may include multiple networks orsub-networks, each of which may include, for example, a wired orwireless data pathway. The network may include a circuit-switched voicenetwork, a packet-switched data network, or any other network able tocarry electronic communications. For example, the network may includenetworks based on the Internet protocol (IP) or asynchronous transfermode (ATM), and may support voice using, for example, VoIP,Voice-over-ATM, or other comparable protocols used for voice, video, anddata communications.

The storage device 106 may be configured to store, manage, or process atleast one of (1) data in a database related to the object being detectedor recognized, and (2) a log of profiles of various devices coupled tothe controller 102 and associated communications including instructions,queries, data, and related metadata. The storage device 106 may compriseof any computer-readable medium known in the art, related art, ordeveloped later including, for example, volatile memory (e.g., RAM),non-volatile memory (e.g., flash, etc.), disk drive, etc., or anycombination thereof. Examples of the storage device 106 may include, butnot limited to, a storage server, a portable storage device (e.g., a USBdrive, an external hard drive, etc.), and so on. The server may beimplemented as any of a variety of computing devices including, forexample, a general-purpose computing device, multiple networked servers(arranged in clusters or as a server farm), a mainframe, or so forth.

The object recognition device 108 may be configured to recognize anobject using various computer vision and machine learning methods knownin the art, related art, or developed later based on various attributesincluding, but not limited to, shape, size, texture, and color of theobject. The object recognition device 108 may include and/or communicatewith an illumination device 110 and may include and/or communicate withan imaging device 112. The illumination device 110 (e.g., compactfluorescent tubes, bulbs, light emitting diodes, etc.) may be configuredto substantially illuminate the object for being recognized by theobject recognition device 108. The imaging device 112 (e.g., a camera, alaser scanner, etc.) may be configured to create or capture an image ofthe illuminated object to be recognized. The created or captured imagemay be processed by the object recognition device 108 to recognize theobject being scanned by the imaging device 112.

The object recognition device 108 may receive multiple such objectimages from the database in the storage device 106, or the imagingdevice 112, or both, as a training dataset corresponding to a variety ofobjects for training the object recognition system 100 so that one ormore images of an object scanned or captured by the imaging device 112are analyzed and recognized by the object recognition device 108.Various features may be extracted from the training dataset. Examples ofthe features may include, but not limited to, shape, size, color,texture, and so on related to the object. The object recognition device108 may apply various known in the art, related art, or developed latermachine learning methods including supervised learning methods (e.g.,Gaussian process regression, Naive Bayes classifier, conditional randomfield, etc.); unsupervised learning methods (e.g.,expectation-maximization algorithm, vector quantization, generativetopographic map, information bottleneck method, etc.); andsemi-supervised learning methods (e.g., generative models, low-densityseparation, graph-based methods, heuristic approaches, etc.) to thetraining dataset for formulating one or more optimized models forrecognizing the objects. During operation, the object recognition device108 may apply the optimized models to the object images received fromthe imaging device 112 to recognize the corresponding objects.

FIG. 2 is a perspective view of an exemplary automated objectrecognition kiosk, according to an embodiment of the present disclosure.In one embodiment, the automated object recognition kiosk 200 mayimplement the object recognition system 100 for retail checkouts. Thekiosk 200 may include a head portion 202, a base portion 204, and asupport panel portion 206 configured to support the head portion 202 andthe base portion 204 of the kiosk 200. The head portion 202, the baseportion 204, and the support panel portion 206 may be made of any rigidand durable material known in the art, related art, or developed laterincluding metals, alloys, composites, and so on, or any combinationthereof, capable of withstanding heat generated by the electronicsintegrated with the kiosk components.

In one embodiment, the head portion 202 may include a top surfaceportion 208, a bottom surface portion 210, and a compartment betweenthem. The compartment may be configured to receive or embed hardwareelectronics. The top surface portion 208 may include a flat portion andan inclined portion having a predetermined slope relative to the flatportion. In one example, the slope may be substantially perpendicular tothe descending line of sight of a user on the inclined portion. Thecompartment may secure the illumination device 110 and the imagingdevice 112. The bottom surface portion 210 may be located opposite tothe base 204 may be substantially flat to avoid shadows being createddue to relative variation in the bottom surface portion 210. Further,the bottom surface portion 210 of the head portion 202 may be locatedopposite to the base portion 204 of the object recognition kiosk 200.The bottom surface portion 210 may be capable of passing light generatedby the illumination device 110 on to the base portion 204 of the objectrecognition kiosk 200. The bottom surface portion 210 may be made up ofor coated with any of the anti-glare materials known in the art, relatedart, or developed later to evenly project light on the object to berecognized. Such coated bottom surface portion 210 may minimize theprojection of shadows due to reflection of illuminated light from theobject and its surroundings. The shadows need to be minimized so thatthe object data (e.g., an object image and its attributes such as color,brightness, texture, etc.) as gathered by the imaging device 112 may beoptimally separated from a predetermined background such as the baseportion 204 by the implemented computer vision and machine learningmethods.

The head portion 202 may include side surfaces such as a side surface212 in communication with lateral edges of the top surface portion 208and the bottom surface portion 210 of the head 202. The side surfacesmay facilitate to reduce unwanted dissipation of generated light intothe ambient surrounding and to focus the generated light on to the baseportion 204 of the object recognition kiosk 200.

In one embodiment, the head portion 202 may be divided into a first part214 and a second part 216, each having a respective top surface 208, abottom surface 210 and a compartment for housing the correspondingelectronic components such as the illumination device 110 and theimaging device 112. The first part 214 and the second part 216 may havea predetermined spacing 218 between them to support electronics forseparate operation based on predetermined aesthetics of the head portion202. At least one of the first part 214 and the second part 216 mayinclude a display device such as an interactive display screen 220 tointeract with a user. Dimensions of the first part 214 may be similar tothe dimensions of the second part 216. However, the relative dimensionsof the first part 214 and the second part 216 may differ from each otherin some embodiments. In further embodiments, the head portion 202 may beintegrated with a variety of payment devices known in the art, relatedart, or developed later. For example, the second part 216 may include apredetermined card reader 220 to receive payments based on the objectbeing recognized by the object recognition kiosk 200. Both the firstpart 214 and the second part 216 may be secured to the support panelportion 206 using various known in the art, related art, or developedlater fastening techniques including a nut and screw arrangement,welding, push-on joint sockets, and so on.

The base portion 204 may refer to any surface, which may be sufficientlyilluminated by the light projected from the head portion 202 of theobject recognition kiosk 200. In some embodiments, the base portion 204may be coated with the anti-glare material for minimizing shadowprojections on the object. In the illustrated embodiment, the baseportion 204 may be coupled to the support panel portion 206 below thehead portion 202 of the object recognition kiosk 200. The base portion204 may have a substantially flat surface opposite to the bottom surfaceportion 210 of the head portion 202 so that an image of the objectplaced on the base portion 204 may be appropriately captured by theimaging device 112. In some embodiments, the base portion 204 may be anelevated surface from the ground and substantially parallel to thebottom surface portion 210. In some other embodiments, a predeterminedregion may be marked or relatively indented uniformly on the baseportion 204 to indicate that the predetermined region is capable ofbeing sufficiently illuminated by the illumination device 110irrespective of ambient lighting conditions. The base portion 204 may besubstantially separated by a predefined distance from the head portion202 for accommodating at least one object in a space, hereinafterreferred to as an examination space 224, between the base portion 204and the head portion 202.

The front side 226 of the examination space 224 may be kept open toallow placement of objects. Rest of the sides of the examination space224 may be left partially or fully open depending on the ambientlighting conditions in which the kiosk 200 is used so that most of thelighting may be provided internally through the kiosk's own lightingsystem such as the illumination device 110. Some tolerance for externalambient lighting may be achieved using a calibration pattern for thebase portion 204 and/or by adjusting various camera properties such asexposure, white balance, and gain.

The calibration pattern may include various colors such as red, green,blue, white, black and their shades or combinations. The calibrationpattern may be implemented as a software program in a computer readablemedium such as a smartcard, which may be integrated, or incommunication, with the object recognition kiosk 200 and used by theobject recognition device 108 to measure the change in ambient lightingand the effect of this lighting change on colors perceived by theimaging device 112. The calibration pattern may also be used by thecontroller 102 to determine the exact position of the imaging devices(e.g., the imaging device 112) relative to the base portion 204, to theobject, and/or to each other. The calibration pattern may be in anyshape such as squares, color wheel or just smeared in any kind of shapeinto the base portion 204.

FIG. 3 is a front view of the exemplary automated object recognitionkiosk 200 of FIG. 2, according to an embodiment of the presentdisclosure. In one embodiment, the support panel portion 206 may includeone or more openings for securing at least one imaging device 112 tocapture an image of the object held between the base portion 204 and thehead portion 202. In the illustrated example, the support panel portion206 may include a first opening 302 securing a first imaging device 304and a second opening 306 securing a second imaging device 308. In someembodiments, at least one of the first imaging device 304 and the secondimaging device 308 may behave as a tracking imaging device to track themovement of a support structure such as a human hand temporarily incommunication with the object for introducing the object to berecognized within the examination space 224 between the base portion 204and the bottom surface portion 210 of the head portion 202. The objectrecognition device 108 analyzes images created or captured by thetracking imaging device to track the movement of the support structure.Other embodiments may include the base portion 204 having one or moremeasurement sensors such as a weight sensor 310 for determining theweight of an object to be recognized upon being placed on the baseportion 204.

Further, the support panel portion 206 may have a slit 312 perpendicularto the spacing 218 between the first part 214 and the second part 216 ofthe head portion 202. The slit 312 may extend along the longitudinalaxis of the support panel portion 206 from a first end of the supportpanel portion 206 to the mid of the support panel portion 206. The firstend of the support panel portion 206 may be adjacent to the head portion202 of the object recognition kiosk 200. The slit 312 may facilitateincorporation of electronics separately for the first imaging device 304and the second imaging device 308 and may support aesthetics of theobject recognition kiosk 200.

FIG. 4 is a portion of the exemplary automated object recognition kiosk200 of FIG. 2, according to an embodiment of the present disclosure. Theillustrated embodiment shows respective compartments in each of thefirst part 214 and the second part 216 of the head portion 202 uponbeing viewed from the bottom surface portion 210. Each of thecompartments may include an imaging region and an illumination region.In one embodiment, the imaging region may be a relatively narrow regiondefined substantially along the edges of the first part 214 and thesecond part 216. The illumination region may be a region surrounded bythe imaging region. The illumination region may have a dimensionsubstantially greater than the dimension of the imaging region.

The imaging region may be configured to secure one or more imagingdevices and the illumination region configured to secure one or moreillumination devices. For example, an imaging region 402 of the firstpart 214 may include imaging devices such as cameras 404-1, 404-2, . . ., 404-n (collectively, cameras 404) and an imaging region 406 of thesecond part 216 may include imaging devices such as cameras 408-1,408-2, . . . , 408-n (collectively, cameras 408). Similarly, a firstillumination region 410 corresponding to the first part 214 may includethe illumination devices such as light emitting diode (LED) lights412-1, 412-2, . . . , 412-n (collectively, LED lights 412) and a secondillumination region 414 corresponding to the second part 216 may includethe illumination devices such as LED lights 416-1, 416-2, . . . , 416-n(collectively, LED lights 416). In a first example, the cameras 404, 408may be two-dimensional cameras (2D cameras) or three-dimensional cameras(3D cameras), or any combination thereof. The 2D cameras may be used tocollect image sequences of objects from multiple viewpoints, and 3Dcameras may be used to get 3D point cloud of objects. Multipleviewpoints facilitate to overcome occlusion as the far side of an objectmay not be visible to an individual camera, or in case there aremultiple objects on the base portion 204 of the kiosk 200, with somepartially or fully hidden from the view of an individual camera. Thecamera properties such as exposure, white balance, gain, focus, pan,tilt, saturation and others may be carefully determined and usuallypre-set during the operation life of the kiosk 200. These cameraproperties may be predefined to values such that changes to the ambientlighting conditions may be partially compensated by adjusting the valuesof these properties.

In a second example, the cameras 404, 408 may be a color video camerasuch as an HD webcam with at least one imaging channel for capturingcolor values for pixels corresponding generally to the primary visiblecolors (typically RGB). In a third example, the cameras 404, 408 may beinfrared cameras with at least one imaging channel for measuring pixelintensity values in the near-infrared (NIR) wavelength range. In afourth example, the cameras 404, 408 may be hybrid devices capable ofcapturing both color and NIR video. In a fifth example, the cameras 404,408 may be multi/hyperspectral cameras capable of capturing images atmultiple wavelength bands.

The cameras 404, 408 may be configured with at least one of the adaptivesteering technology and the controlled steering technology known in theart, related art, or developed later for maneuvering the direction ofthe imaging device 112 for capturing images based on the position of theobject within the examination space 224. Further, the intensity of theLED lights 412, 416 may be sufficiently high so that the ambient lightreceived by the examination space 224 and/or the base portion 204 isminimal. The light generated by the LED lights 412, 416 may besubstantially white light so that colors of the objects to be recognizedmay be optimally visible and captured by the cameras 404, 408.

The automated object recognition kiosk 200 may be implemented indifferent business scenarios, such as for retail checkouts. For this,the automated object recognition kiosk 200 may be trained to obtain amodel using various computer vision and machine learning methods knownin the art, related art, or developed later. The obtained model may bestored in the storage device 106 and applied by the object recognitiondevice 108 for recognizing products such as one or more fresh foodsincluding, but not limited to, fresh fruits and vegetables, dairyproducts, freshly prepared eatables such as curries, breads, pastas,salads, and burgers; or any combination thereof.

In order to train the kiosk 200, the controller 102 may (1) configure apredetermined calibration pattern based on the ambient lightingconditions, (2) initialize predefined or dynamically defined attributesof the cameras and the LED lights based on the ambient lightingconditions, (3) calibrate relative positions of the cameras with respectto each other and/or at least one of the base portion 204 and theproduct; and (4) adaptively tune the LED lights to generate relativelywhite light for illuminating the base portion 204 to a predeterminedlevel of brightness, upon the kiosk 200 being switched ON. Thepredetermined brightness level of the illuminated base portion 204 maybe relatively greater than the brightness of the ambient light enteringinto the examination space 224 between the head portion 202 and the baseportion 204 of the kiosk 200. Subsequently, the automated objectrecognition kiosk 200 may be fed with details of inventory productsincluding packaged as well as fresh products in a retail store eitherdirectly through the interactive display screen 220, or via a connectionto a point of sale (POS) terminal (not shown) over the network. Thedetails may include product name, product type, price, manufacturingdate, expiry date, batch identification number, quantity, packagedimensions, etc., and may be stored in an inventory or object databasein the storage device 106.

One or more products for which the kiosk 200 need to be trained, suchproducts may be introduced within the examination space 224 by a user.In one example, one or more products such as fresh items, which may notbe covered with an opaque covering such as a package cover, a humanhand, etc., may be introduced within the examination space 224. Theproducts may be exposed to the light generated by the illuminationdevice 110 such as the LEDs 412, 416 and the imaging devices such as thecameras 404, 408. Each product may be placed in multiple positions andorientations at a predefined location such as on a predetermined regionof the base portion 204. The placed product may be directly imaged byone or more imaging devices such as the cameras 404, 408 to capturemultiple images of the products. The captured images may be stored inthe storage device 106 of the kiosk 200.

The controller 102 may be configured to feed the captured images as atraining dataset to the object recognition device 108, which may beconfigured to extract multiple features (e.g., brightness, contrast,hue, size, shape, texture, etc.) from the captured images of theproducts. The object recognition device 108 may use extracted featuresas inputs to a predetermined computer vision and machine learning methodthat may formulate an optimized model based on the extracted features.The optimized model may be saved in the storage device 106 by the objectrecognition device 108. Similarly, the automated object recognitionkiosk 200 may be trained for various package covers used to pack orcarry or hold the products, for example, the fresh items.

In order to recognize the product, the object recognition kiosk 200 maybe configured with relatively the same values for at least one of theinitialization parameters being implemented for training the kiosk 200.Examples of these initialization parameters include, but not limited to,calibration pattern, attributes of the cameras 304, 308, 404, 408 andthe LED lights 412, 416, relative positions of the cameras 304, 308,404, 408, brightness level of the LED lights 412, 416. However, in someembodiments, the values of the initialization parameters may vary fromtheir training values based on the ambient light conditions and relativepositions of the cameras 304, 308, 404, 408, the base portion 204, andthe products to be recognized.

A user may introduce one or more products within the examination space224 of the automated object recognition kiosk 200. Multiple cameras ofthe kiosk 200 may simultaneously capture multiple images of the productfrom different positions and orientations. The captured images may befed to the object recognition device 108 by the controller 102. Theobject recognition device 108 may extract multiple features from thereceived images and apply the optimized model stored in the storagedevice 106 to these extracted features for recognizing the product basedon the inventory product details stored in the storage device 106. Uponrecognizing the product, the controller 102 may provide a visual, audioor textual indication to a user. For example, the controller 102 mayprovide a pop-up message on the interactive display screen 220 with abeep to indicate a user that the product has been recognized.Additionally, the controller 102 may provide related details of therecognized product including, but not limited to, name, type, quantity,price, etc., on the display screen for the user. Some embodiments inwhich the product was placed on the kiosk base portion 204 equipped witha weight sensor, the controller 102 may display the weight of theproduct on the interactive display screen 220. In some embodiments, thecontroller 102 may provide the indication regarding the product on oneor more computing devices such as a mobile phone of the user over thenetwork. The user may use the received indication to pay for the productat a payment device such as a credit card reader, which may beintegrated with the kiosk 200, or at a POS terminal in communicationwith the kiosk 200, for completing the product purchase and the relatedtransaction. In some embodiments, the payment device or the POS terminalmay not be in communication with the kiosk 200.

In order to return a purchased product, the user may re-introduce theproduct within the examination space 224. The object recognition device108 may recognize the product using the optimized model as discussedabove and provide an indication to the user. Based on the indication, apredetermined amount may be returned to the user as per one or morepredefined criteria either directly by asking the user to swipe hiscredit or debit card against a card reader or by a cashier at the POSterminal. Examples of the predefined criteria may include, but notlimited to, the product being rescanned by the cameras may be returnedonly within two hours from the time of purchase; the package cover ofthe purchase product should not be tampered with for the product beingreturned; products may not be eligible for return after purchase, etc.

Exemplary embodiments are intended to cover all software or computerprograms capable of performing the various heretofore-discloseddeterminations, calculations, etc., for the disclosed purposes. Forexample, exemplary embodiments are intended to cover all software orcomputer programs capable of enabling processors to implement thedisclosed processes. In other words, exemplary embodiments are intendedto cover all systems and processes that configure a computing device toimplement the disclosed processes. Exemplary embodiments are alsointended to cover any and all currently known, related art or laterdeveloped non-transitory recording or storage mediums (such as a CD-ROM,DVD-ROM, hard drive, RAM, ROM, floppy disc, magnetic tape cassette,etc.) that record or store such software or computer programs. Exemplaryembodiments are further intended to cover such software, computerprograms, systems and/or processes provided through any other currentlyknown, related art, or later developed medium (such as transitorymediums, carrier waves, etc.), usable for implementing the exemplaryoperations disclosed above.

In accordance with the exemplary embodiments, the disclosed computerprograms may be executed in many exemplary ways, such as an applicationthat is resident in the storage device 106 of a device or as a hostedapplication that is being executed on a server or mobile computingdevice, and communicating with the device application or browser via anumber of standard protocols, such as TCP/IP, HTTP, XML, SOAP, REST,JSON and other sufficient protocols. The disclosed computer programs maybe written in exemplary programming languages that execute from memoryon the computing device or from a hosted server, such as BASIC, COBOL,C, C++, Java, Pascal, or scripting languages such as JavaScript, Python,Ruby, PHP, Perl or other sufficient programming languages.

The above description does not provide specific details of manufactureor design of the various components. Those of skill in the art arefamiliar with such details, and unless departures from those techniquesare set out, techniques, known, related art or later developed designsand materials should be employed. Those in the art are capable ofchoosing suitable manufacturing and design details.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.It will be appreciated that several of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be combined intoother systems or applications. Various presently unforeseen orunanticipated alternatives, modifications, variations, or improvementstherein may subsequently be made by those skilled in the art withoutdeparting from the scope of the present disclosure as encompassed by thefollowing claims.

What is claimed is:
 1. A checkout system comprising: a base; a headportion; a support that supports the head portion above the base, anexamination space being defined between the base and the head portionfor accommodating a plurality of food items in the examination space; anillumination device, disposed in the head portion, for illuminating theplurality of food items; at least one imaging device, disposed in thehead portion, for capturing a plurality of color images of the pluralityof food items; a hardware processor, coupled to the at least one imagingdevice, for applying a machine-learning model for performing imagerecognition of the plurality of food items in the plurality of colorimages to identify each food item of the plurality of food items, theimage recognition being based on a plurality of features of the fooditems that include shape, size, and color; and a display device forpresenting an identification of each of the food items.
 2. The checkoutsystem of claim 1, wherein the plurality of features comprises a textureof the food items.
 3. The checkout system of claim 1, wherein amachine-learning program formulates the machine-learning model withsupervised learning using a training dataset comprising reference imagesof food items.
 4. The checkout system of claim 1, wherein the hardwareprocessor calculates a price for each food item and a total price forall the food items in the examination space, the checkout systemcomprising a card reader to effect payment of the total price for allthe food items.
 5. The checkout system of claim 1, comprising: a fooditem database including food item names and prices, wherein the hardwareprocessor accesses the food item database for each identified food itemand causes presentation, on the display device, of a name and a price ofeach identified food item.
 6. The checkout system of claim 5, comprisinga weight sensor disposed in the base for measuring a weight of one ormore food items, wherein the price of the one or more food items isbased on the measured weight.
 7. The checkout system of claim 1, whereinthe at least one imaging device comprises at least one colorthree-dimensional camera or at least one color two-dimensional camera.8. The checkout system of claim 1, wherein the at least one imagingdevice comprises at least one color two-dimensional camera and at leastone color three-dimensional camera.
 9. The checkout system of claim 1,wherein the illumination device generates white light.
 10. The checkoutsystem of claim 1, wherein the checkout system adaptively tunes theillumination device to generate light that illuminates the plurality offood items with a predetermined level of brightness.
 11. The checkoutsystem of claim 1, wherein a top surface of the base comprises acalibration pattern, wherein the hardware processor determines aposition of the at least one imaging device relative to the base basedon images, taken by the at least one imaging device, of the calibrationpattern.
 12. The checkout system of claim 1, wherein each feature isassociated with a property of each food item, the food items being atleast one of fresh food items and packaged food items.
 13. The checkoutsystem of claim 1, wherein the at least one imaging device capturescolor images of food items placed in one or more orientations in theexamination space for training the machine-learning model.
 14. Thecheckout system of claim 1, wherein the machine-learning model istrained to recognize food items selected from a group consisting ofpackaged goods, fruits, vegetables, and fresh food, the fresh foodcomprising one or more of curries, breads, pasta, salads, and burgers.15. The checkout system of claim 1, wherein the plurality of colorimages is captured after placing the plurality of food items in theexamination space.
 16. A method comprising: illuminating an examinationspace for accommodating a plurality of food items in a checkout system,the examination space being defined between a base and a head portion ofthe checkout system; capturing, by at least one imaging device mountedin the head portion, a plurality of color images of the plurality offood items; applying, by a hardware processor of the checkout system, amachine-learning model to perform image recognition of the plurality offood items in the plurality of color images to identify each food itemof the plurality of food items, the image recognition being based on aplurality of features of the food items that include shape, size, andcolor; and presenting, on a display device of the checkout system, anidentification of each of the identified food items.
 17. The method ofclaim 16, wherein the plurality of features comprises a texture of thefood items.
 18. The method of claim 16, wherein a machine-learningprogram generates the machine-learning model by supervised learningusing a training dataset comprising reference images of food items. 19.The method of claim 16, comprising: calculating, by the hardwareprocessor, a price for each food item and a total price for all the fooditems in the examination space, the checkout system comprising a cardreader to effect payment of the total price for all the food items. 20.The method of claim 16, comprising: accessing, by the hardwareprocessor, a food item database including food item names and prices toobtain a name and price of each identified food item; and presenting, onthe display device, the name and the price of each identified food item.21. The method of claim 16, comprising: weighting, by a weight sensordisposed in the base, at least one food item; and calculating the priceof the at least one food items based on the measured weight.
 22. Themethod of claim 16, comprising capturing at least one three-dimensionalcolor image with at least one three-dimensional color camera.
 23. Themethod of claim 16, comprising capturing at least one two-dimensionalcolor image with at least one two-dimensional color camera.
 24. Themethod of claim 16, wherein the illumination device generates whitelight, the method comprising: adaptively tunings the illumination deviceto generate light that illuminates the plurality of food items with apredetermined level of brightness.
 25. The method of claim 16,comprising: determining, by the hardware processor, a position of the atleast one imaging device relative to the base using a calibrationpattern of the base.
 26. The method of claim 16, wherein each feature isassociated with a property of each food item, the food items being atleast one of fresh food items and packaged food items.
 27. The method ofclaim 16, wherein the at least one image capturing device captures colorimages of food items placed in one or more positions in the examinationspace for training the machine-learning model.
 28. The method of claim16, wherein the machine-learning model is trained to recognize fooditems selected from a group consisting of packaged goods, fruits,vegetables, and fresh food, the fresh food comprising one or more ofcurries, breads, pasta, salads, and burgers.
 29. The method of claim 16,wherein the plurality of color images is captured after placing theplurality of food items in the examination space.
 30. A non-transitorymachine-readable storage medium comprising instructions that, whenexecuted by a machine, cause the machine to perform operationscomprising: illuminating an examination space for accommodating aplurality of food items in a checkout system, the examination spacebeing defined between a base and a head portion of the checkout system;capturing, by at least one imaging device mounted in the head portion, aplurality of color images of the plurality of food items; applying, by ahardware processor of the checkout system, a machine-learning model toperform image recognition of the plurality of food items in theplurality of color images to identify each food item of the plurality offood items, the image recognition being based on a plurality of featuresof the food items that include shape, size, and color; and presenting,on a display device of the checkout system, an identification of each ofthe identified food items.